{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019年12月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append('../../py')\n",
    "import db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Salary_Mean</th>\n",
       "      <th>Salary_Median</th>\n",
       "      <th>JD_Count</th>\n",
       "      <th>HeadCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>201906</td>\n",
       "      <td>12990</td>\n",
       "      <td>12000</td>\n",
       "      <td>98669</td>\n",
       "      <td>323172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>201907</td>\n",
       "      <td>13209</td>\n",
       "      <td>12000</td>\n",
       "      <td>94918</td>\n",
       "      <td>308802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>201908</td>\n",
       "      <td>13388</td>\n",
       "      <td>12500</td>\n",
       "      <td>94569</td>\n",
       "      <td>303830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>201909</td>\n",
       "      <td>13421</td>\n",
       "      <td>12500</td>\n",
       "      <td>90804</td>\n",
       "      <td>292998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>201910</td>\n",
       "      <td>13337</td>\n",
       "      <td>12500</td>\n",
       "      <td>87620</td>\n",
       "      <td>284579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>201911</td>\n",
       "      <td>13451</td>\n",
       "      <td>12500</td>\n",
       "      <td>84685</td>\n",
       "      <td>277672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>201912</td>\n",
       "      <td>13554</td>\n",
       "      <td>12500</td>\n",
       "      <td>84141</td>\n",
       "      <td>276906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  Salary_Mean  Salary_Median  JD_Count  HeadCount\n",
       "0  201906        12990          12000     98669     323172\n",
       "1  201907        13209          12000     94918     308802\n",
       "2  201908        13388          12500     94569     303830\n",
       "3  201909        13421          12500     90804     292998\n",
       "4  201910        13337          12500     87620     284579\n",
       "5  201911        13451          12500     84685     277672\n",
       "6  201912        13554          12500     84141     276906"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_data = pd.read_sql(sql='select * from MonthlyStats order by Month', con=conn)\n",
    "stat_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MGz+v5fHPlnDu099yRf9EbjuzK40j/Fz/3F2F3DJlLt8u28rl/dpw/4ieVWL4LBLpS3Diq+e+XsEjMzK5/cwu/H6Ibuu4v+27i/j7F0t5ZfYa6sbG8MczunD1wLYROZS2YP12Rr+WRnb+Xh44rydX9NdJJX4o66mskfcvTKLGV0uyefTTTM5NaskfTlEwHEzDOjWYMLwnM8adSK82jXjgw0UMe+pbvl22xe/Sjsi0tCwueu4HSgKON0cPUjBEAIWD+GLllp3c8sZcurVowKMXJ0XtpGtF6dy8Pq/8tj//HplCYUmAa174mRtfSWXNtl1+l3ZYhcUB/vLeAm5/ax59E+P48ObB9NalzCNC1RvQFd/tKChi1Ktp1IipxqRrkqvkvEJ5mBln9GjOSV2a8uJ3q3nmy2Wc8cQsbjixPX84pVPYfTdgc34BY15LY87aPEad1IE7z+pK9QgcDquqwutfk0S9QMDxx6nprNq6i9duGECbxnX8Lini1Kwew5ghHbmwbwKPzMjkua9X8HZaFncN7cYFfRLCYnju51U5/P71OewuLOaZK/twblIrv0uSI6QYl0r198+X8vnibP5yTncGdWzidzkRrXmDWjxxaW/e/f0JtGxUm9vemseFz/1A+ro832pyzvG/36/iyn//SP1a1XnvD79RMEQohYNUmhkLNvKPL5dzSXJrrj2hnd/lRI0+iXG8O+YEHr+kF+vz9nD+s99z25vzyM4vqNQ69hSWcOvUdB74cBFDujbj/bG/oUvz+pVag1QcncoqlSJzUz4X/vMHujSvz9SbBlKzus5tPxZ27i0O3oXuu1XUiDHGntqZ3w5ud8z/vtds28VNr6axZPMObjtDpyWHM93sR8JG3u5CRjzzPQVFJXx482DdFL4SrN66i4c+XszMRZtp26QO957Tg9O7H5u70H2Vmc24KXMxM566vDdDujar8H1IxdH3HCQsFJcEGDt5Lpu2F/D8NckKhkrSrmld/j0yhVdv6E+NmGrc+EoqI1/8mWWbd1TYPgIBx1OfL+O3L/9C67g6fDh2sIIhiigc5Jj62yeZfLd8Kw+efxx9E+NKX0Eq1Imd4/lk3IlMGN6DeevyGPrUtzzw4UK27y46qu1u31PEja+k8uTnS7mgdwJvjzmBxCY68yya6FRWOWbemZPFf75bxbWD2nJpvzZ+l1Nl1YipxvW/ac+IXq14YuZSXv5hNe+nb+C2M7tweb/EI74LXeamfEa/mkZW7h4eGNGTkYPa6kuMUUhHDnJMZGTlcfc78xnYoTH3ntvD73IEaFKvJg9dcDwf3jyYTs3qMf7dBZz79Hf8tHJbmbfxwbwNXPDsD+wqLGHKqIFce0I7BUOUUjhIhduyYy83vZpGfL2aPHtl34i8SFw069mqIVNHDeTZK/uSv6eIyyb9yB8mzyErd/ch1ykqCTBx+iJueWMuPVs14KObB5NShW9rWhVoWEkqVGFxgDGvpZG7u5C3x5xAk3o1/S5JDsLMOCepJad2a8akWSt57pvlfL5oM6NP7sjokztSO/a/p75u2bGXsZPn8NOqHK47oR1/Htad2OoK/Gin37BUqPs/XEjqmlweu7gXPVs19LscKUXt2BjGnd6ZL24bwhk9mvPUF8s47fGv+XDeBpxzzFmby/Cnv2NeVh5PXtaL+0f0VDBUEfqeg1SY139aw/h3FzD65I7cfXY3v8uRcvh5VQ4PfLiQhRvyOT6hIZmb8mnRsBbPX52ssI8S+p6DVKpfVucw4f2FDOkazx1ndfW7HCmn/u0b88HYwTx84fFsyNvD4E5N+XDsYAVDFaQ5BzlqG/L2MOa1NNo0rsNTl/c54lMjJbzEVDOu6J/IZSltdAmMKkzhIEeloKiEm15No6AowJRRyTSsXcPvkqSCKBiqNoWDlJtzjnvemc/89dv598gUOjXTFThFooXmHKTcXvhuFe/OXc+fzujCGT2a+12OiFQghYOUy7fLtvD/Pl7M0J4tGHtKJ7/LEZEKpnCQI7Zm2y7GTp5L52b1efzSXhqbFolCCgc5Irv2FjPqlTQAJo1Mpm6Y3dReRCqGwkHKLBBw3PbmPJZl7+DZK/vStkldv0sSkWNE4SBl9sxXy5mxcBN/HtadwZ2b+l2OiBxDpYaDmb1oZtlmtiCkbaKZZZhZupl9ZmatvPbzQtpTzWxwyDrXmtky7+fakPZkM5tvZsvN7B+m6/+GpZmLNvPEzKVc0CeBGwa397scETnGynLk8BIwdL+2x5xzSc653sB04D6v/Qugl9f+W+A/AGbWGJgADAD6AxPMbN9twZ4DRgGdvZ/99yU+W569gz9OTSepdUMevvB4Xb9fpAooNRycc7OAnP3a8kOe1gWc177T/fdKfr+2A2cBM51zOc65XGAmMNTMWgINnHOzvfVeAc4/mg5JxQreDjKNWjWq8fzVydSqEVP6SiIS8cp9qomZPQSMBLYDp4S0XwA8DDQDzvGaE4B1IatneW0J3uP92w+1z1EEjzJITEwsb+lSRiUBxy1vzCUrdzeTbxxIq0a1/S5JRCpJuSeknXPjnXNtgNeBsSHt7zrnuhE8ApjoNR9sHMIdpv1Q+5zknEtxzqXEx8eXt3Qpo8c+XcI3S7dw/4ie9NNdv0SqlIo4W2kycNH+jd5wVEcza0rwiCD0DvOtgQ1ee+uDtIvPPpi3gee/WcGVAxK5akBbv8sRkUpWrnAws84hT0cAmV57p31nG5lZXyAW2AZ8CpxpZnHeRPSZwKfOuY3ADjMb6K03Eni/3L2RCrFg/XbunDaPfu3iuH94T7/LEREflDrnYGZvAEOApmaWRfCso2Fm1hUIAGuA0d7iFwEjzawI2ANc5k0055jZROAXb7m/Ouf2TXKPIXhGVG3gE+9HfLJt515uejWNuDqx/POqZN0SUqSK0m1C5VdFJQGu/s9PpK/L463Rg0hq3cjvkkSkgpX1NqG6MI786sHpi/hpVQ5PXtZLwSBSxWnMQAB485d1vDx7Db8b3J4L+rQufQURiWoKB2HO2lzufW8BJ3Zuyt1nd/O7HBEJAwqHKm5zfgGjX02jRcNaPH1FH6rH6J+EiCgcqrSCohJuejWNnXuL+ffIFBrVifW7JBEJE5qQrqKcc/zlvQWkr8vj+av70rVFfb9LEpEwoiOHKurlH1bzVloWt5zaiaHHtfS7HBEJMwqHKuiHFVuZ+NFiTu/enFtP7+J3OSIShhQOVcy6nN384fU5tG9alycv60W1aro3g4gcSOFQhewuLGbUq2kUBxyTrkmmfq0afpckImFKE9JVhHOOO6dlkLkpn/+9rh8d4uv5XZKIhDEdOVQRz32zgukZG7nzrG4M6drM73JEJMwpHKqArzKzeezTJQzv1YrRJ3fwuxwRiQAKhyi3cstObpkyl+4tGvDoRUl4t9sQETkshUMU21FQxI2vpFIjphqTRiZTOzbG75JEJEJoQjpKBQKOP05NZ/W23bx2wwBax9XxuyQRiSA6cohST36+lM8XZ3PfuT0Y1LGJ3+WISIRROEShT+Zv5Okvl3NpSmtGDmrrdzkiEoEUDlEmc1M+t701jz6JjZh4/nGagBaRclE4RJHcXYXc+Eoq9WpW5/mrk6lZXRPQIlI+mpCOEsUlAca+MYfN2/cy9aaBNG9Qy++SRCSCKRyixMOfZPL98m08enESfRLj/C5HRCKchpWiwNtpWbzw3SquO6Edl6a08bscEYkCCocIN29dHve8O59BHZow/pzufpcjIlFC4RDBsncUcNOracTXq8mzV/WlRox+nSJSMTTnEKGKSgL8/rU55O0p5O0xJ9C4bqzfJYlIFCn1o6aZvWhm2Wa2IKRtopllmFm6mX1mZq289qu89gwz+8HMeoWsM9TMlpjZcjO7O6S9vZn9ZGbLzGyqmeldrgz+9c0KUtfk8shFSfRs1dDvckQkypRlHOIlYOh+bY8555Kcc72B6cB9Xvsq4GTnXBIwEZgEYGYxwLPA2UAP4Aoz6+Gt8wjwpHOuM5AL3FD+7lQNSzbt4KkvlnFOUkvO653gdzkiEoVKDQfn3CwgZ7+2/JCndQHntf/gnMv12n8EWnuP+wPLnXMrnXOFwBTgPAt+ffdUYJq33MvA+eXsS5VQXBLgjmnzqF+rBn8d0dPvckQkSpV7zsHMHgJGAtuBUw6yyA3AJ97jBGBdyGtZwACgCZDnnCsOaT/kR2EzGwWMAkhMTCxv6RHt39+uIiNrO89c2Ycm9Wr6XY6IRKlyn97inBvvnGsDvA6MDX3NzE4hGA537Ws62CYO036ofU5yzqU451Li4+PLV3gEW569kyc/X8rQni045/iWfpcjIlGsIs59nAxctO+JmSUB/wHOc85t85qzgNBvZ7UGNgBbgUZmVn2/dtlPScBxx7R51ImN0QX1ROSYK1c4mFnnkKcjgEyvPRF4B7jGObc0ZJlfgM7emUmxwOXAB845B3wFXOwtdy3wfnlqinYvfreKuWvzuH94T+LrazhJRI6tUucczOwNYAjQ1MyygAnAMDPrCgSANcBob/H7CM4j/NP7ZFvsDQMVm9lY4FMgBnjRObfQW+cuYIqZPQjMBV6oqM5Fi5VbdvI/ny3h9O7NOa93K7/LEZEqwIIf3iNPSkqKS01N9buMYy4QcFw2aTZLNu3g8z+dTDNdbVVEjoKZpTnnUkpbTtdbCHMvz17NL6tzuW94TwWDiFQahUMYW7NtF4/OWMKQrvFc1FdfdhORyqNwCFOBgOOutzOoXs14+MLjdXaSiFQqhUOYev2nNfy4Mod7z+1Oy4a1/S5HRKoYhUMYWpezm4c/yeTEzk118x4R8YXCIcw457jnnfkY8LeLkjScJCK+UDiEmSm/rOO75Vu5Z1h3EhppOElE/KFwCCPr8/bw0EeLGdShCVf2r5oXFhSR8KBwCBP7hpNKAo5HLkqiWjUNJ4mIfxQOYeKttCxmLd3C3Wd3I7FJHb/LEZEqTuEQBjZtL2Di9EX0b9+Yawa29bscERGFg9+cc4x/dz5FJQEe1XCSiIQJhYPP3ktfzxeZ2dx+ZlfaNa3rdzkiIoDCwVfZ+QXc/8Ei+iY24vrftPe7HBGRXykcfOKc4973FrCnqITHLulFjIaTRCSMKBx88mHGRj5btJnbzuhCx/h6fpcjIvJ/KBx8sHXnXia8v4BebRrxuxM7+F2OiMgBFA4+mPD+QnbtLeF/Lk7ScJKIhCWFQyX7eP5GPpq/kXGnd6Zz8/p+lyMiclAKh0qUs6uQv7y3gOMTGnLTSRpOEpHwVd3vAqqS+z9YSH5BEa9fMoDqMcplEQlfeoeqJJ8t3MQH8zYw9pTOdGvRwO9yREQOS+FQCfJ2FzL+vQV0b9mA35/S0e9yRERKpWGlSvDX6YvI3VXI/17XjxoaThKRCKB3qmPsy8zNvDNnPWOGdOS4hIZ+lyMiUiYKh2No+54i7nlnPl2b12fsqZ38LkdEpMxKDQcze9HMss1sQUjbRDPLMLN0M/vMzFp57d3MbLaZ7TWz2/fbzlAzW2Jmy83s7pD29mb2k5ktM7OpZhZbkR3000MfLWLrzkIeuySJmtVj/C5HRKTMynLk8BIwdL+2x5xzSc653sB04D6vPQe4Bfif0IXNLAZ4Fjgb6AFcYWY9vJcfAZ50znUGcoEbytGPsPPN0i28mZrFqJM6kNS6kd/liIgckVLDwTk3i+CbfmhbfsjTuoDz2rOdc78ARfttpj+w3Dm30jlXCEwBzjMzA04FpnnLvQycX56OhJMdBUXc83YGnZrVY9xpnf0uR0TkiJX7bCUzewgYCWwHTill8QRgXcjzLGAA0ATIc84Vh7QnHGafo4BRAImJieUrvBI8/Ekmm/ILmDbmBGrV0HCSiESeck9IO+fGO+faAK8DY0tZ/GBXl3OHaT/UPic551Kccynx8fFlL7YSfb98K5N/WsvvTuxA38Q4v8sRESmXijhbaTJwUSnLZAFtQp63BjYAW4FGZlZ9v/aItGtvMXe9nUGHpnX50xld/C5HRKTcyhUOZhY6kD4CyCxllV+Azt6ZSbHA5cAHzjkHfAVc7C13LfB+eWoKB4/MyGR93h4evThJw0kiEtFKnXMwszeAIUBTM8sCJgDDzKwrEADWAKO9ZVsAqUADIGBmt6PftEsAAAozSURBVAI9nHP5ZjYW+BSIAV50zi30dnEXMMXMHgTmAi9UYP8qzY8rt/HK7DVc/5t2pLRr7Hc5IiJHxYIf3iNPSkqKS01N9bsMAHYXFnP2U9/iHMy49UTqxOqqJCISnswszTmXUtpyeherAI99uoQ123YzZdRABYOIRAVdPuMopa7O4aUfVjNyUFsGdmjidzkiIhVC4XAUCopKuHNaBgmNanPX0G5+lyMiUmE0BnIUnpi5lJVbd/H67wZQt6b+KkUkeujIoZzmrM3lP9+u5Ir+ifymU1O/yxERqVAKh3IoKCrhjrfm0aJBLf48TMNJIhJ9NBZSDk99sYwVW3bx8m/7U79WDb/LERGpcDpyOEIZWXlMmrWSS1Nac3KX8Ly+k4jI0VI4HIG9xSXc8VYGTevFMv6cHqWvICISoTSsdASe/XI5Szbv4MXrUmhYW8NJIhK9dORQRgvWb+efX6/gwj4JnNqtud/liIgcUwqHMigsDnDHtAzi6sZy33ANJ4lI9NOwUhk89/UKFm/MZ9I1yTSqE+t3OSIix5yOHEqRuSmfZ75axoherTizZwu/yxERqRQKh8MoLglwx1sZNKxdg/tH9PS7HBGRSqNhpcP416yVzF+/nX9e1ZfGdTWcJCJVh44cDmHp5h089fkyzjm+JcOOb+l3OSIilUrhcBDFJcGzk+rVqs4D52k4SUSqHg0rHcQL361i3ro8/nFFH5rWq+l3OSIilU5HDvtZsWUnj89cypk9mjM8ScNJIlI1KRxClAQcd07LoHaNGB684DjMzO+SRER8oWGlEC/9sJq0Nbk8eVkvmtWv5Xc5IiK+0ZGDZ/XWXTz2aSandWvG+b0T/C5HRMRXCgcgEHDc+XYGNWKq8dAFx2s4SUSqPIUD8OqPa/h5VQ5/ObcHLRpqOElEpMqHw7qc3TwyI5OTusRzSXJrv8sREQkLpYaDmb1oZtlmtiCkbaKZZZhZupl9ZmatvHYzs3+Y2XLv9b4h61xrZsu8n2tD2pPNbL63zj+sEsd0At7ZSdXM+NuFGk4SEdmnLEcOLwFD92t7zDmX5JzrDUwH7vPazwY6ez+jgOcAzKwxMAEYAPQHJphZnLfOc96y+9bbf1/HzOSf1zJ75TbGn9OdVo1qV9ZuRUTCXqnh4JybBeTs15Yf8rQu4LzH5wGvuKAfgUZm1hI4C5jpnMtxzuUCM4Gh3msNnHOznXMOeAU4/6h7VQZZubt5+OPFDO7UlMv7tamMXYqIRIxyf8/BzB4CRgLbgVO85gRgXchiWV7b4dqzDtJ+qH2OIniUQWJiYnlLxznHPe/MxwEPazhJROQA5Z6Qds6Nd861AV4HxnrNB3uXdeVoP9Q+JznnUpxzKfHx8Uda8q/eTF3Ht8u2cs/Z3WjTuE65tyMiEq0q4mylycBF3uMsIHSMpjWwoZT21gdpP2Y2bt/Dg9MXM7BDY64a0PZY7kpEJGKVKxzMrHPI0xFApvf4A2Ckd9bSQGC7c24j8ClwppnFeRPRZwKfeq/tMLOB3llKI4H3y9uZ0uwbTioOOB65KIlq1TScJCJyMKXOOZjZG8AQoKmZZRE862iYmXUFAsAaYLS3+MfAMGA5sBu4HsA5l2NmE4FfvOX+6pzbN8k9huAZUbWBT7yfY6Ik4OjavD6ndG1G2yZ1j9VuREQingVPEoo8KSkpLjU11e8yREQiipmlOedSSluuyn9DWkREDqRwEBGRAygcRETkAAoHERE5gMJBREQOoHAQEZEDKBxEROQACgcRETlAxH4Jzsy2EPx2dnk0BbZWYDl+ipa+REs/QH0JV9HSl6PtR1vnXKlXLo3YcDgaZpZalm8IRoJo6Uu09APUl3AVLX2prH5oWElERA6gcBARkQNU1XCY5HcBFSha+hIt/QD1JVxFS18qpR9Vcs5BREQOr6oeOYiIyGEoHERE5AARFQ5m1sbMvjKzxWa20MzGee2NzWymmS3z/ozz2ruZ2Wwz22tmt++3rXFmtsDbzq0h7QfdlvfaEDNL99b5JhL7YWZ3eH1I99YrMbPGEdqXhmb2oZnN89a5vrz9CIO+xJnZu2aWYWY/m9lxEdCXS7y2gJml7LfOPWa23MyWmNlZkdgPM2vi7XunmT1T3j6ESV/OMLM0M5vv/XlqqQU75yLmB2gJ9PUe1weWAj2AR4G7vfa7gUe8x82AfsBDwO0h2zkOWADUIXir1M+Bzt5rh9pWI2ARkLhv25HYj/3qGA58GcG/kz+HPI4HcoDYCO3LY8AE73E34IsI+L10B7oCXwMpIev0AOYBNYH2wAogJgL7URcYTPA2yM8cze8jDPrSB2gVsv760uqNqCMH59xG59wc7/EOYDGQAJwHvOwt9jJwvrdMtnPuF6Bov011B350zu12zhUD3wAXeK8ddFvAlcA7zrm1+7Ydof0IdQXwRnn7EQZ9cUB9MzOgHsFwKI7QvvQAvvC2mwm0M7Pm4dwX59xi59ySg+z+PGCKc26vc24VwXvK94+0fjjndjnnvgMKylN7mPVlrnNug/d0IVDLzGoert6ICodQZtaOYBr+BDR3zm2E4C+AYOIezgLgJO+wsQ4wDGjjvXaobXUB4szsa++wbGSE9mPffusAQ4G3K6If3jbbUbl9eYbgf5QNwHxgnHMuEKF9mQdc6O27P9AWaB3mfTmUBGBdyPMsr+2o+NCPY8bnvlwEzHXO7T3cQtWPYINhw8zqEXxTu9U5lx/84Fh2zrnFZvYIMBPYSfA/ZmmfOKsDycBpQG1gtpn96JxbeqT17+NTP/YZDnzvnMs5op0egk99OQtIB04FOgIzzexb51z+kdYfyqe+/A14yszSCQbd3DKsUyqf+nKwnRzVOfM+/1+pUH72xcx6Ao8AZ5a2bMQdOZhZDYJ/sa87597xmjebWUvv9ZZAqUM+zrkXnHN9nXMnERyOWFbKtrKAGd6h5lZgFtArAvuxz+Uc5ZDSPj725XqCQ33OObccWEVwvD7i+uKcy3fOXe+c6w2MJDiHsirM+3IoWfzfT7KtCR7dlYuP/ahwfvbFzFoD7wIjnXMrSls+osLBG1t+AVjsnHsi5KUPgGu9x9cC75dhW828PxMJHs7ve6M81LbeB040s+reodwAgmOGkdYPzKwhcHJZtl+G/fvZl7UEj+Twxue7AisjsS9m1sjMYr323wGzjuYIqJL6cigfAJebWU0zaw90Bn4+sh78um8/+1Gh/OyLmTUCPgLucc59X6aC3VHOwFfmD8EzBxyQQXA4IZ3geFsTgpN5y7w/G3vLtyD4KSYfyPMeN/Be+5bg2UfzgNNC9nHQbXmv3eGts4DgIWGk9uM6ghOGEf07AVoBnxEchlkAXB3BfRnktWUC7wBxEdCXC7zl9gKbgU9DXhtP8CylJcDZEdyP1QQ/me/0lukRiX0B7gV2hew3nVLOuNTlM0RE5AARNawkIiKVQ+EgIiIHUDiIiMgBFA4iInIAhYOIiBxA4SAiIgdQOIiIyAH+P+NRR3lC1orIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\"Month\",\"Salary_Mean\",data=stat_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.16855095451835353, 0.12158437672257658, 0.08637350522538333, 0.08426985251932098, 0.07470779476449214, 0.0455378938724084, 0.41897562237746505]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts=[]\n",
    "percentages=[]\n",
    "count1=conn.execute(f\"select COUNT(1) from _{year}{month:02}\").fetchall()[0][0]\n",
    "counts.append(count1)\n",
    "\n",
    "for i in range(1,month-6+1):\n",
    "    i_count=conn.execute(f\"select COUNT(1) from _{year}{month:02} a inner join _{year}{month-i:02} b on a.job_id=b.job_id\").fetchall()[0][0]\n",
    "    counts.append(i_count)\n",
    "    percentages.append((counts[i-1]-i_count)/counts[0])\n",
    "    \n",
    "percentages.append(counts[-1]/counts[0])\n",
    "print(percentages)\n",
    "\n",
    "labels=[]\n",
    "for i in range(1,month-6+1):\n",
    "    labels.append(i)\n",
    "labels.append(f\"{str(month-6+1)}+\")\n",
    "\n",
    "plt.pie(percentages, labels=labels)\n",
    "plt.title(\"Age of Jobs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 按照只能统计平均工资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "区块链工程师 平均工资为21385\n"
     ]
    }
   ],
   "source": [
    "sql=f'select AVG(monthly_salary)  from _{year}{month} where expert_blockchain=1 and monthly_salary>0'\n",
    "salary_blockchain=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'区块链工程师 平均工资为{salary_blockchain}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "算法工程师 平均工资为19161\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month} where monthly_salary>0 and career='算法工程师'\"\n",
    "salary_algorithm=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'算法工程师 平均工资为{salary_algorithm}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系统架构师 平均工资为23319\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month} where monthly_salary>0 and career='系统架构师'\"\n",
    "salary_architect=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'系统架构师 平均工资为{salary_architect}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Java工程师 平均工资为14991\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month} where monthly_salary>0 and pl_java=1\"\n",
    "salary_java=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'Java工程师 平均工资为{salary_java}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python工程师 平均工资为21111\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month} where monthly_salary>0 and pl_python=1\"\n",
    "salary_python=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'Python工程师 平均工资为{salary_python}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_func = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_func['职能']=['区块链工程师', '算法工程师','系统架构师', 'Java工程师',  'Python工程师']\n",
    "data_func['工资']=[salary_blockchain, salary_algorithm, salary_architect, salary_java, salary_python]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职能</th>\n",
       "      <th>工资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>系统架构师</td>\n",
       "      <td>23319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>区块链工程师</td>\n",
       "      <td>21385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Python工程师</td>\n",
       "      <td>21111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>算法工程师</td>\n",
       "      <td>19161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Java工程师</td>\n",
       "      <td>14991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          职能     工资\n",
       "2      系统架构师  23319\n",
       "0     区块链工程师  21385\n",
       "4  Python工程师  21111\n",
       "1      算法工程师  19161\n",
       "3    Java工程师  14991"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_func.sort_values(by='工资', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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